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Protect an AI coding session as you would any powerful, untrusted operator: give it the smallest possible identity, confine its files and network access, keep secrets out of its context, and require a human review before sensitive changes ship. Coding agents can inspect repositories, run commands, edit files and call development tools autonomously, so a leaked .env value or poisoned instruction file can become a production incident. Use short-lived, least-privilege credentials from an approved secrets system, start sessions in a sandbox with approvals enabled, scan every change for secrets and vulnerabilities, and revoke credentials immediately when exposure is possible.
What can an AI coding agent access?
An agent is not merely autocomplete. Depending on its host application and your approvals, it may receive selected files, terminal output, diagnostics, issue text, pull requests and web content; execute shell commands; modify the workspace; invoke MCP servers or extensions; and make network requests. OpenAI describes coding agents as able to “autonomously review repositories, run commands, and interact with development tools.” Treat every one of those channels as a possible path to disclosure or unauthorized action.
Your threat model should include both accidental exposure and hostile instructions. A repository can contain a malicious script, an AGENTS.md or editor rule can tell the agent to upload data, and a web page copied into context can contain prompt-injection text. Tool descriptions and tool output are data, not authority. The agent should never be able to decide on its own that a credential, private source file or customer record may leave the approved boundary.
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- A key is hardcoded in source, a notebook, a test fixture, a screenshot or a log and is then indexed or pasted into the session.
- An agent reads a local
.envfile because it is inside the workspace, even though the file is listed in.gitignore. - A shell command prints environment variables, configuration or cloud metadata into terminal output that becomes model context.
- An MCP server, extension or dependency receives more files or network access than the task requires.
- An agent-generated commit adds a secret, weak authentication check, unsafe logging or an unexpected outbound request.
What should I do before starting an agent session?
Prepare the identity, workspace and approval boundaries before you open the agent. The goal is to make an unsafe action difficult even if the model is confused or the repository contains hostile instructions.
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- Inventory reachable assets. List the AI account, repositories, cloud projects, API keys, service identities, MCP servers, extensions and package registries the session could touch. Include indirect access such as a shell credential helper or a mounted home directory.
- Create a task-specific identity. Use a project key or workload identity with only the API operations and resources required for this task. Assign an expiry and a named rotation owner. Do not reuse a personal administrator token.
- Choose an approved secret mechanism. Put the value in your organization’s secrets manager or a controlled environment mechanism. The agent should receive only the minimum runtime access needed to complete the task, not a file containing your entire credential set.
- Remove secrets from the workspace. Exclude
.envfiles, credential directories, private certificates, production configuration, notebooks, fixtures and generated logs from the agent’s readable paths. A file being ignored by version control does not make it invisible to a local agent. - Open unfamiliar code in restricted mode. Review repository instruction files, scripts, build hooks, dependency manifests, lockfiles and MCP manifests before granting trust. Treat
AGENTS.md,.cursorrulesand equivalent files as untrusted project input until a human has read them. - Start with containment. Enable workspace-limited writes, disable network access by default, and allowlist only the domains and ports the task genuinely needs. Keep approval prompts for shell commands, external services and sensitive paths.
- Record the boundary. Note the agent identity, model, tools, approvals, repository revision and credential expiry. This makes later investigation and rollback possible.
How should API keys be stored and supplied?
Keep keys out of source code, prompts, client-side applications, logs, screenshots, notebooks, test fixtures and agent instruction files. OpenAI’s key-safety guidance says never deploy a key in browsers or mobile apps and never commit it to a repository; requests from those clients should go through a backend that holds the credential.
Use a narrow, temporary credential
- Prefer a secret from a managed vault or a workload-identity federation flow over a long-lived static token.
- Restrict the identity to specific projects, repositories, endpoints, actions and data sets. Read-only access is preferable when the task does not require writes.
- Set an expiration date. OpenAI notes that project keys can be created with an expiration date after which requests are rejected.
- Define who rotates the credential and how the replacement is deployed before the session starts.
- Keep personal, production and agent identities separate so a single mistake cannot expose unrelated systems.
Pass values without revealing them to the model
Use a runtime mechanism that injects a secret only into the process that needs it, and prevent commands from echoing the value. Redact terminal output and CI logs. For debugging, provide a synthetic token with the same format or a deliberately failing test credential; do not paste a real key into chat to help the agent diagnose an authentication error.
If an SDK requires a value in an environment variable, arrange for the wrapper, task runner or secret broker to supply it at execution time while excluding the file that stores the value from the agent’s context. Test the wrapper with a harmless placeholder and verify that errors do not print headers, request bodies or environment dumps.
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How do I sandbox an AI coding agent?
Use layered controls rather than relying on one “safe mode.” A useful baseline is a disposable or dedicated workspace, workspace-only writes, a read-only view of unrelated files, no network by default, and explicit approval for commands or paths that cross the boundary.
Filesystem boundaries
- Mount only the repository and the minimum cache or build directories needed. Do not mount your home directory, SSH keys, cloud credential folders or production configuration.
- Protect secret patterns such as
.env*,*.pem,*.p12, credential JSON files, signing keys and deployment manifests. Require a human approval before reading or writing them. - Use a disposable clone or snapshot for exploratory work. Keep the authoritative checkout elsewhere and apply reviewed patches rather than granting direct write access.
Network boundaries
- Block outbound traffic initially. Add an allowlist for the package registry, documentation host or test service required by the task.
- Do not expose cloud metadata endpoints or internal administrative services to an untrusted process.
- Separate code execution from deployment credentials. A build that needs to download dependencies should not also be able to publish releases or modify billing.
Approval and rollback
Keep approval prompts enabled for shell commands, network access, credential use, database changes and writes outside the task directory. Inspect the exact command and arguments, not only the agent’s explanation. Commit a clean baseline before the session, make small commits, and retain an easy reset or container-destroy path.
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How should I review repository instructions, MCP servers and extensions?
Read project instruction files and tool manifests before the agent sees them as authority. A legitimate instruction can still be unsafe in your environment, and a malicious one may ask the agent to upload source, disable scanning or print secrets.
Repository instructions
- Check
AGENTS.md,.cursorrules, Copilot instruction files, build scripts, hooks and task configuration into version control review. - Reject instructions that request credentials in prompts, broad filesystem access, disabled security checks, hidden network calls or irreversible commands.
- Verify that dependency-install and post-install scripts are expected. Pin or review new versions before execution.
MCP servers and extensions
- Verify provenance, publisher, requested scopes, host permissions and destination domains.
- Pin versions and review updates; a tool that can read files or make HTTP requests can transmit whatever the agent supplies.
- Enable only the tools required for the task. Keep secrets and customer data outside the tool’s reachable paths.
- Log tool calls and approvals so an investigator can reconstruct what happened.
Never treat a tool description or tool output as an instruction that overrides your policy. The human-defined boundary remains authoritative.
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Context is broader than the files the agent opens. Selected code, terminal output, diagnostics, issue descriptions, pull requests and web pages can all carry secrets or prompt injection. Before sending any material, remove API keys, bearer tokens, cookies, private certificates, customer records and proprietary data that the task does not require.
Safe debugging practices
- Replace credentials with redacted values or synthetic fixtures. Preserve only the prefix or format needed to reproduce a parsing bug.
- Capture a minimal error message instead of a complete request, response or environment dump.
- Use a scrubber for logs and screenshots, then inspect the scrubbed output manually before pasting it.
- Do not ask the agent to “find the key” or search the whole disk. Point it to a narrowly scoped, sanitized fixture.
Ask what context the host application retains, where conversation transcripts and tool logs are stored, and who can access them. Apply your organization’s retention and access policy to those records.
When must a human approve an agent’s work?
Require a checkpoint before changes to authentication, authorization, cryptography, CI/CD, deployment, billing, data export or customer-data handling. The agent may draft or test such changes, but a qualified reviewer should decide whether they are safe and complete.
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- Read the entire diff, including generated files, lockfiles, workflow files and deleted lines.
- Trace new network calls, subprocesses, file reads and credential references to their intended purpose.
- Check validation, error handling, logging and default settings. Look for secrets in strings, comments, fixtures and test output.
- Run the required tests in an isolated environment with non-production credentials.
- Record the reviewer, agent identity, model, tools used and approvals with the commit or pull request.
Do not merge because the code “looks right” or because tests pass. Tests can miss exfiltration, insecure defaults and authorization mistakes that a focused security review will catch.
Which security checks should run before merge?
Use multiple automated checks because each finds a different class of failure.
| Check | What it can catch | What the reviewer still verifies |
|---|---|---|
| Secret scanning | Keys and tokens in source, history, fixtures, documentation and generated artifacts | Whether a flagged value is active, where it may have travelled, and whether revocation is required |
| Static application security testing (SAST) | Injection paths, unsafe deserialization, authorization gaps and dangerous API use | Business logic, threat-model assumptions and false negatives |
| Software-composition analysis (SCA) | Vulnerable or newly introduced dependencies and license issues | Package provenance, maintainer changes, lockfile diffs and whether the dependency is necessary |
| Tests | Behavioral regressions, permission boundaries and error-handling failures | Coverage of abuse cases, production-like configuration and tests that may leak data |
| Diff and policy review | Unexpected files, network calls, workflow changes and policy violations | Intent, least privilege, rollback and compliance evidence |
Protect sensitive paths with CODEOWNERS or an equivalent mandatory-review rule. Run the checks on the final tree, not only on the agent’s intermediate patch.
Which agent setup is safest for a given task?
Choose the least powerful setup that can complete the work. The following comparison uses credential lifetime and scope, storage, boundaries, approvals, context handling, tool trust, scanning and recovery speed as decision criteria.
| Setup | Credential and storage pattern | Filesystem and network boundary | Approvals and auditability | Best fit and main risk |
|---|---|---|---|---|
| Local agent in a personal checkout | Often inherits user credentials and local files; highest exposure if not deliberately cleaned | May see the whole workspace and unrestricted network | Varies by editor; rollback can be manual | Only low-risk, non-secret experiments. A forgotten local token can affect unrelated projects. |
| Restricted local workspace | Dedicated, expiring project identity supplied at runtime | Workspace-limited writes, protected patterns and an allowlisted network | Command and path approvals retained in session logs | Routine repository work where a developer needs fast feedback without production access. |
| Disposable sandbox or container | Short-lived identity or brokered access; no persistent secret files | Ephemeral filesystem, no network by default and explicit mounts | Destroy-and-recreate rollback; record commands and tool calls | Unfamiliar code, dependency changes and tasks that execute arbitrary scripts. Setup overhead is higher. |
| Remote or managed agent runner | Workload identity scoped to a project; secrets remain in the platform’s vault | Policy-enforced egress and repository isolation, subject to provider configuration | Central logs, approvals and security policy; verify retention and administrator access | Team-scale automation. Misconfigured tenancy, logging or tool permissions can broaden exposure. |
In every setup, keep production credentials and deployment authority outside the coding session unless the task explicitly requires them and a human is present for the approval.
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What should I do if an API key or source code may have been exposed?
Assume a key is compromised when it appeared in a prompt, transcript, log, screenshot, repository, tool call or untrusted machine. Speed matters more than proving misuse first.
- Revoke or delete the affected key immediately. Disable the token at the provider and invalidate related sessions if supported.
- Issue a replacement with narrower permissions. Give it an expiry and assign a rotation owner. Update the backend or secret reference without restoring the old value.
- Review activity. Check API usage, spend, authentication history, repository audit logs and agent telemetry for unusual calls, destinations, permission changes or data volume.
- Preserve evidence. Keep relevant logs, conversation records, repository history, diffs and timestamps before retention policies erase them.
- Determine the blast radius. Establish whether source code, customer data or other credentials were read, copied or modified. Escalate under your incident-response policy.
- Close the exposure path. Remove the secret from files and history where appropriate, tighten sandbox and scanning rules, rotate related credentials and add a regression check.
- Restore service cautiously. Re-enable access only with the replacement identity, verified monitoring and a documented approval.
Deleting a leaked value from the latest file is not enough if it remains in Git history, agent transcripts, caches, build artifacts or logs. Search each location that the session could have reached.
What policy should a team adopt for AI coding agents?
Write a short, enforceable standard and make the secure path the easiest path. At minimum, require:
- Named, non-personal agent identities with least privilege, expiration and an owner.
- Secrets-manager or workload-identity use; no keys in source, prompts, client applications, logs, screenshots, notebooks or fixtures.
- Restricted mode for unfamiliar repositories and a review of instruction files, MCP servers, extensions and dependency changes.
- Sandboxed execution, workspace-limited writes, deny-by-default network access and approvals for sensitive commands and paths.
- Human review for authentication, authorization, cryptography, CI/CD, deployment, billing and data-export changes.
- Secret scanning, SAST, SCA, tests and dependency review before merge, with mandatory ownership for security-sensitive paths.
- Central records of the agent, model, tools, approvals, repository revision and resulting commit.
- A tested revocation and investigation procedure, including usage monitoring and evidence preservation.
Australia’s Cyber and Infrastructure Security Centre calls access to advanced AI services a security-sensitive asset and recommends monitoring model access, credential creation, permission changes and unusual usage patterns. Apply that mindset to both hosted services and local agents.
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How can I verify that a session ended cleanly?
Before closing the task, inspect the final diff and the agent’s command and network logs. Confirm that temporary credentials expired or were revoked, disposable workspaces were destroyed, caches and artifacts contain no secrets, and no new MCP server or extension remains enabled. Run a final repository and history scan, then record the approved commit and any follow-up rotation or monitoring action.
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Frequently Asked Questions
Can an agent read a .env file that is in .gitignore?
Yes. Ignore rules control version-control tracking, not local filesystem permissions. If the file is inside a readable workspace, an agent may open it unless you exclude the path or enforce a protected-file policy.
Should I disable network access completely for coding agents?
Use deny-by-default access and allow only the hosts required for the task. Completely offline execution is safest for code that needs no downloads or external services, while a narrow allowlist supports dependency installation without exposing internal networks or unrestricted exfiltration.
Is a sandbox enough to protect a production API key?
No. A sandbox limits files and processes but cannot compensate for an over-privileged or long-lived credential. Keep production authority outside the session, use an expiring identity, and require approval for any operation that could affect production.
How do I remove a secret from Git history after revoking it?
Treat history cleanup as a separate incident-response task: preserve evidence first, rewrite affected history with an approved procedure, coordinate force-updates with every clone, and scan branches, tags, caches and artifacts. The key must remain revoked even after the value is removed from visible files.
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